Fuzzy Integration of Support Vector Regression Models for Anticipatory Control of Complex Energy Systems

Abstract

Anticipatory control systems are a class of systems whose decisions are based on predictions for the future state of the system under monitoring. Anticipation denotes intelligence and is an inherent property of humans that make decisions by projecting in future. Likewise, intelligent systems equipped with predictive functions may be utilized for anticipating future states of complex systems, and therefore facilitate automated control decisions. Anticipatory control of complex energy systems is paramount to their normal and safe operation. In this paper a new intelligent methodology integrating fuzzy inference with support vector regression is introduced. The proposed methodology implements an anticipatory system aiming at controlling energy systems in a robust way. Initially, a set of support vector regressors is adopted for making predictions over critical system parameters. The predicted values are used as input to a two-stage fuzzy inference system that makes decisions regarding the state of the energy system. The inference system integrates the individual predictions at its first stage, and outputs a decision together with a certainty factor computed at its second stage. The certainty factor is an index of the significance of the decision. The proposed anticipatory control system is tested on a real-world set of data obtained from a complex energy system, describing the degradation of a turbine. Results exhibit the robustness of the proposed system in controlling complex energy systems.